Reinforcement Learning (RL) how to obtain $p(s',r|s,a)$
The function $r(s,a,s')$ gives the expected reward in each scenario, but not the distribution of rewards that lead to values $r_{search}$ and $r_{wait}$ 

The text explains that reward is $+1$ for each can found, and that different distributions of numbers of cans are expected when waiting as opposed to searching. However, it does not give any description of the actual distributions, just summarises them as the two expected rewards, and suggests $r_{search} \gt r_{wait}$

You have two main ways to answer the exercise:


Invent some parameters for the distributions of $r_{search}$ and $r_{wait}$ in order to split up single values of $p(s'|s,a)$ into multiple values of $p(s', r|s,a)$. E.g you could decide that $r_{search}$ consists of $0  \eta_0 + 1  \eta_1 + 2 \eta_2 + 3 \eta_3$ where $\eta_0, \eta_1, \eta_2, \eta_3$ are probabilities that sum to $1$ - each row that currently has $r_{search}$ as the output of $r(s,a,s')$ would then split into 4 rows with reward 0, 1, 2, 3 to complete the new table . . . $r_{wait}$ would need a different set of parameters.
Ignore the details of the distribution, move column $r(s,a,s′)$ to the left and call it $r$, changing $p(s|s,a)$ to $p(s', r|s,a)$. It might be all that's expected given the lack of information.


My personal opinion is that the authors want you to think about solution 1 - the only issue is that it requires you to invent some new parameters that were not provided. The ones I name are only a suggestion, they do not represent a specific "correct" answer in terms provided by the book, because the book omits those details.

As an example to start with, if you start solution 1, and use parameters as I have labelled them, you will end up with a first row looking like this:

$s\qquad \qquad a\qquad  \qquad  s'\qquad  \qquad   r \qquad       p(s', r| s, a)$
$high  \qquad \quad search  \qquad  high  \qquad \quad   0  \qquad   \alpha \eta_0$